Damage assessment for tropical cyclones landing in Guangdong Province of China by using a projection pursuit dynamic cluster model

Chaoyong Tu (), Shumin Chen (), Zhongkuo Zhao (), Weibiao Li () and Changjian Ni ()
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Chaoyong Tu: Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
Shumin Chen: Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
Zhongkuo Zhao: China Meteorological Administration (CMA)
Weibiao Li: Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)
Changjian Ni: Chengdu University of Information Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 114, issue 1, No 20, 475-493

Abstract: Abstract Using data from 62 tropical cyclones (TCs) that made landfall in Guangdong Province (China) between 2000 and 2019, we selected six indices—minimum central pressure, maximum wind speed, maximum rainstorm ratio, cumulative surface rainfall, tropical cyclone (TC) track length, lifetime—and constructed a projection pursuit dynamic cluster (PPDC) model to assess TC damage risk. Although a single index may provide correct information on the intensity of certain types of damage, a comprehensive damage risk assessment cannot be obtained from individual indices alone. The PPDC model is a stable tool for TC damage risk assessment, especially in terms of economic loss, agricultural disaster area and disaster-affected population. Model validation improved the correlation of each of the indices. Output from the PPDC model for disaster-affected population and agricultural disaster-affected area also improved after model validation. We examined the limitations of the single indices using data from three TCs. Output from the PPDC model can closely reflect the intensity of the damage caused by the cyclones. Projection pursuit dynamic clustering is a new objective method for TC damage risk assessment, which can provide the scientific basis to support disaster prevention and mitigation.

Keywords: Tropical cyclone; Damage; Disaster assessment; Projection pursuit dynamic cluster (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-022-05398-5

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